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Record W4408266980 · doi:10.33423/jlae.v22i1.7536

How and When Technostress Predicts Abusive Supervisor Behavior in Remote Work: An Integrative Theoretical Framework

2025· article· en· W4408266980 on OpenAlex
Ayesha Tabassum, Daniela Petrovski

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Leadership Accountability and Ethics · 2025
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsYork University
Fundersnot available
KeywordsTechnostressSupervisorAbusive supervisionWork (physics)PsychologySocial psychologyApplied psychologyManagementEconomicsEngineering

Abstract

fetched live from OpenAlex

Technostress has become prevalent among employees due to the rise of remote work. While there is a plethora of research on the outcomes of technostress, its impact on abusive supervisor behavior has not been thoroughly explored. This study uses the transactional theory of stress and coping to propose a framework explaining how technostress can lead to abusive supervisor behavior in the context of remote work. The framework suggests that supervisors experience anxiety and anger as a result of technostress, which can lead them to engage in abusive behavior as a way of coping with these negative emotions. The study also suggests that supervisors' personality and moral traits can influence the relationship between technostress and abusive behavior. This framework contributes to understanding abusive supervision and expands the research on its causes and boundary conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.399
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it